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Wild Bootstrap Inference for Event Studies

Source: R/bootstrap.R
bootstrap_test.Rd

Computes bootstrap p-values for AAR and CAAR test statistics using the wild bootstrap approach. Per-firm random weights preserve the cross-sectional dependence structure while randomizing the sign of abnormal returns under the null hypothesis.

Usage

bootstrap_test(
  task,
  n_boot = 999L,
  weight_type = "rademacher",
  statistic = "both",
  group = NULL,
  seed = NULL
)

Arguments

task

A fitted EventStudyTask with abnormal returns computed.

n_boot

Number of bootstrap replications. Default 999.

weight_type

Type of bootstrap weights: "rademacher" (default, +1/-1 with equal probability) or "mammen" (Mammen two-point distribution).

statistic

Which statistic to bootstrap: "aar", "caar", or "both" (default).

group

Optional group name to filter.

seed

Optional seed for reproducibility.

Value

A tibble with columns: relative_index, observed_aar, observed_caar, boot_p_aar, boot_p_caar.

See also

Other eventstudy-statistics: ARTTest, BHARTTest, BMPTest, CARTTest, CSectTTest, CalendarTimePortfolioTest, GeneralizedSignTest, KolariPynnonenTest, MultiEventStatisticsSet, PatellZTest, RankTest, SignTest, SingleEventStatisticsSet, StatisticsSetBase, TestStatisticBase, adjust_p_values(), car_by_group(), car_quantiles(), cross_sectional_regression(), model_diagnostics(), pretrend_test(), simulate_event_study(), validate_task()